import contextlib
import numbers
from itertools import chain, product
from numbers import Integral
from operator import getitem

import numpy as np

from ..base import tokenize
from ..highlevelgraph import HighLevelGraph
from ..utils import _deprecated, derived_from, random_state_data, skip_doctest
from .core import (
    Array,
    asarray,
    broadcast_shapes,
    broadcast_to,
    normalize_chunks,
    slices_from_chunks,
)
from .creation import arange


@_deprecated()
def doc_wraps(func):
    """Copy docstring from one function to another"""

    def _(func2):
        if func.__doc__ is not None:
            func2.__doc__ = skip_doctest(func.__doc__)
        return func2

    return _


class RandomState:
    """
    Mersenne Twister pseudo-random number generator

    This object contains state to deterministically generate pseudo-random
    numbers from a variety of probability distributions.  It is identical to
    ``np.random.RandomState`` except that all functions also take a ``chunks=``
    keyword argument.

    Parameters
    ----------
    seed: Number
        Object to pass to RandomState to serve as deterministic seed
    RandomState: Callable[seed] -> RandomState
        A callable that, when provided with a ``seed`` keyword provides an
        object that operates identically to ``np.random.RandomState`` (the
        default).  This might also be a function that returns a
        ``randomgen.RandomState``, ``mkl_random``, or
        ``cupy.random.RandomState`` object.

    Examples
    --------
    >>> import dask.array as da
    >>> state = da.random.RandomState(1234)  # a seed
    >>> x = state.normal(10, 0.1, size=3, chunks=(2,))
    >>> x.compute()
    array([10.01867852, 10.04812289,  9.89649746])

    See Also
    --------
    np.random.RandomState
    """

    def __init__(self, seed=None, RandomState=None):
        self._numpy_state = np.random.RandomState(seed)
        self._RandomState = RandomState

    def seed(self, seed=None):
        self._numpy_state.seed(seed)

    def _wrap(
        self, funcname, *args, size=None, chunks="auto", extra_chunks=(), **kwargs
    ):
        """Wrap numpy random function to produce dask.array random function

        extra_chunks should be a chunks tuple to append to the end of chunks
        """
        if size is not None and not isinstance(size, (tuple, list)):
            size = (size,)

        shapes = list(
            {
                ar.shape
                for ar in chain(args, kwargs.values())
                if isinstance(ar, (Array, np.ndarray))
            }
        )
        if size is not None:
            shapes.append(size)
        # broadcast to the final size(shape)
        size = broadcast_shapes(*shapes)
        chunks = normalize_chunks(
            chunks,
            size,  # ideally would use dtype here
            dtype=kwargs.get("dtype", np.float64),
        )
        slices = slices_from_chunks(chunks)

        def _broadcast_any(ar, shape, chunks):
            if isinstance(ar, Array):
                return broadcast_to(ar, shape).rechunk(chunks)
            if isinstance(ar, np.ndarray):
                return np.ascontiguousarray(np.broadcast_to(ar, shape))

        # Broadcast all arguments, get tiny versions as well
        # Start adding the relevant bits to the graph
        dsk = {}
        lookup = {}
        small_args = []
        dependencies = []
        for i, ar in enumerate(args):
            if isinstance(ar, (np.ndarray, Array)):
                res = _broadcast_any(ar, size, chunks)
                if isinstance(res, Array):
                    dependencies.append(res)
                    lookup[i] = res.name
                elif isinstance(res, np.ndarray):
                    name = f"array-{tokenize(res)}"
                    lookup[i] = name
                    dsk[name] = res
                small_args.append(ar[tuple(0 for _ in ar.shape)])
            else:
                small_args.append(ar)

        small_kwargs = {}
        for key, ar in kwargs.items():
            if isinstance(ar, (np.ndarray, Array)):
                res = _broadcast_any(ar, size, chunks)
                if isinstance(res, Array):
                    dependencies.append(res)
                    lookup[key] = res.name
                elif isinstance(res, np.ndarray):
                    name = f"array-{tokenize(res)}"
                    lookup[key] = name
                    dsk[name] = res
                small_kwargs[key] = ar[tuple(0 for _ in ar.shape)]
            else:
                small_kwargs[key] = ar

        sizes = list(product(*chunks))
        seeds = random_state_data(len(sizes), self._numpy_state)
        token = tokenize(seeds, size, chunks, args, kwargs)
        name = f"{funcname}-{token}"

        keys = product(
            [name], *([range(len(bd)) for bd in chunks] + [[0]] * len(extra_chunks))
        )
        blocks = product(*[range(len(bd)) for bd in chunks])

        vals = []
        for seed, size, slc, block in zip(seeds, sizes, slices, blocks):
            arg = []
            for i, ar in enumerate(args):
                if i not in lookup:
                    arg.append(ar)
                else:
                    if isinstance(ar, Array):
                        arg.append((lookup[i],) + block)
                    else:  # np.ndarray
                        arg.append((getitem, lookup[i], slc))
            kwrg = {}
            for k, ar in kwargs.items():
                if k not in lookup:
                    kwrg[k] = ar
                else:
                    if isinstance(ar, Array):
                        kwrg[k] = (lookup[k],) + block
                    else:  # np.ndarray
                        kwrg[k] = (getitem, lookup[k], slc)
            vals.append(
                (_apply_random, self._RandomState, funcname, seed, size, arg, kwrg)
            )

        meta = _apply_random(
            self._RandomState,
            funcname,
            seed,
            (0,) * len(size),
            small_args,
            small_kwargs,
        )

        dsk.update(dict(zip(keys, vals)))

        graph = HighLevelGraph.from_collections(name, dsk, dependencies=dependencies)
        return Array(graph, name, chunks + extra_chunks, meta=meta)

    @derived_from(np.random.RandomState, skipblocks=1)
    def beta(self, a, b, size=None, chunks="auto", **kwargs):
        return self._wrap("beta", a, b, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def binomial(self, n, p, size=None, chunks="auto", **kwargs):
        return self._wrap("binomial", n, p, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def chisquare(self, df, size=None, chunks="auto", **kwargs):
        return self._wrap("chisquare", df, size=size, chunks=chunks, **kwargs)

    with contextlib.suppress(AttributeError):

        @derived_from(np.random.RandomState, skipblocks=1)
        def choice(self, a, size=None, replace=True, p=None, chunks="auto"):
            dependencies = []
            # Normalize and validate `a`
            if isinstance(a, Integral):
                # On windows the output dtype differs if p is provided or
                # absent, see https://github.com/numpy/numpy/issues/9867
                dummy_p = np.array([1]) if p is not None else p
                dtype = np.random.choice(1, size=(), p=dummy_p).dtype
                len_a = a
                if a < 0:
                    raise ValueError("a must be greater than 0")
            else:
                a = asarray(a)
                a = a.rechunk(a.shape)
                dtype = a.dtype
                if a.ndim != 1:
                    raise ValueError("a must be one dimensional")
                len_a = len(a)
                dependencies.append(a)
                a = a.__dask_keys__()[0]

            # Normalize and validate `p`
            if p is not None:
                if not isinstance(p, Array):
                    # If p is not a dask array, first check the sum is close
                    # to 1 before converting.
                    p = np.asarray(p)
                    if not np.isclose(p.sum(), 1, rtol=1e-7, atol=0):
                        raise ValueError("probabilities do not sum to 1")
                    p = asarray(p)
                else:
                    p = p.rechunk(p.shape)

                if p.ndim != 1:
                    raise ValueError("p must be one dimensional")
                if len(p) != len_a:
                    raise ValueError("a and p must have the same size")

                dependencies.append(p)
                p = p.__dask_keys__()[0]

            if size is None:
                size = ()
            elif not isinstance(size, (tuple, list)):
                size = (size,)

            chunks = normalize_chunks(chunks, size, dtype=np.float64)
            if not replace and len(chunks[0]) > 1:
                err_msg = (
                    "replace=False is not currently supported for "
                    "dask.array.choice with multi-chunk output "
                    "arrays"
                )
                raise NotImplementedError(err_msg)
            sizes = list(product(*chunks))
            state_data = random_state_data(len(sizes), self._numpy_state)

            name = "da.random.choice-%s" % tokenize(
                state_data, size, chunks, a, replace, p
            )
            keys = product([name], *(range(len(bd)) for bd in chunks))
            dsk = {
                k: (_choice, state, a, size, replace, p)
                for k, state, size in zip(keys, state_data, sizes)
            }

            graph = HighLevelGraph.from_collections(
                name, dsk, dependencies=dependencies
            )
            return Array(graph, name, chunks, dtype=dtype)

    # @derived_from(np.random.RandomState, skipblocks=1)
    # def dirichlet(self, alpha, size=None, chunks="auto"):

    @derived_from(np.random.RandomState, skipblocks=1)
    def exponential(self, scale=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("exponential", scale, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def f(self, dfnum, dfden, size=None, chunks="auto", **kwargs):
        return self._wrap("f", dfnum, dfden, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def gamma(self, shape, scale=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("gamma", shape, scale, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def geometric(self, p, size=None, chunks="auto", **kwargs):
        return self._wrap("geometric", p, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def gumbel(self, loc=0.0, scale=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("gumbel", loc, scale, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def hypergeometric(self, ngood, nbad, nsample, size=None, chunks="auto", **kwargs):
        return self._wrap(
            "hypergeometric", ngood, nbad, nsample, size=size, chunks=chunks, **kwargs
        )

    @derived_from(np.random.RandomState, skipblocks=1)
    def laplace(self, loc=0.0, scale=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("laplace", loc, scale, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def logistic(self, loc=0.0, scale=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("logistic", loc, scale, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def lognormal(self, mean=0.0, sigma=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("lognormal", mean, sigma, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def logseries(self, p, size=None, chunks="auto", **kwargs):
        return self._wrap("logseries", p, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def multinomial(self, n, pvals, size=None, chunks="auto", **kwargs):
        return self._wrap(
            "multinomial",
            n,
            pvals,
            size=size,
            chunks=chunks,
            extra_chunks=((len(pvals),),),
        )

    @derived_from(np.random.RandomState, skipblocks=1)
    def negative_binomial(self, n, p, size=None, chunks="auto", **kwargs):
        return self._wrap("negative_binomial", n, p, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def noncentral_chisquare(self, df, nonc, size=None, chunks="auto", **kwargs):
        return self._wrap(
            "noncentral_chisquare", df, nonc, size=size, chunks=chunks, **kwargs
        )

    @derived_from(np.random.RandomState, skipblocks=1)
    def noncentral_f(self, dfnum, dfden, nonc, size=None, chunks="auto", **kwargs):
        return self._wrap(
            "noncentral_f", dfnum, dfden, nonc, size=size, chunks=chunks, **kwargs
        )

    @derived_from(np.random.RandomState, skipblocks=1)
    def normal(self, loc=0.0, scale=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("normal", loc, scale, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def pareto(self, a, size=None, chunks="auto", **kwargs):
        return self._wrap("pareto", a, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def permutation(self, x):
        from .slicing import shuffle_slice

        if isinstance(x, numbers.Number):
            x = arange(x, chunks="auto")

        index = np.arange(len(x))
        self._numpy_state.shuffle(index)
        return shuffle_slice(x, index)

    @derived_from(np.random.RandomState, skipblocks=1)
    def poisson(self, lam=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("poisson", lam, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def power(self, a, size=None, chunks="auto", **kwargs):
        return self._wrap("power", a, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def randint(self, low, high=None, size=None, chunks="auto", dtype="l", **kwargs):
        return self._wrap(
            "randint", low, high, size=size, chunks=chunks, dtype=dtype, **kwargs
        )

    @derived_from(np.random.RandomState, skipblocks=1)
    def random_integers(self, low, high=None, size=None, chunks="auto", **kwargs):
        return self._wrap(
            "random_integers", low, high, size=size, chunks=chunks, **kwargs
        )

    @derived_from(np.random.RandomState, skipblocks=1)
    def random_sample(self, size=None, chunks="auto", **kwargs):
        return self._wrap("random_sample", size=size, chunks=chunks, **kwargs)

    random = random_sample

    @derived_from(np.random.RandomState, skipblocks=1)
    def rayleigh(self, scale=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("rayleigh", scale, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def standard_cauchy(self, size=None, chunks="auto", **kwargs):
        return self._wrap("standard_cauchy", size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def standard_exponential(self, size=None, chunks="auto", **kwargs):
        return self._wrap("standard_exponential", size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def standard_gamma(self, shape, size=None, chunks="auto", **kwargs):
        return self._wrap("standard_gamma", shape, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def standard_normal(self, size=None, chunks="auto", **kwargs):
        return self._wrap("standard_normal", size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def standard_t(self, df, size=None, chunks="auto", **kwargs):
        return self._wrap("standard_t", df, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def tomaxint(self, size=None, chunks="auto", **kwargs):
        return self._wrap("tomaxint", size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def triangular(self, left, mode, right, size=None, chunks="auto", **kwargs):
        return self._wrap(
            "triangular", left, mode, right, size=size, chunks=chunks, **kwargs
        )

    @derived_from(np.random.RandomState, skipblocks=1)
    def uniform(self, low=0.0, high=1.0, size=None, chunks="auto", **kwargs):
        return self._wrap("uniform", low, high, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def vonmises(self, mu, kappa, size=None, chunks="auto", **kwargs):
        return self._wrap("vonmises", mu, kappa, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def wald(self, mean, scale, size=None, chunks="auto", **kwargs):
        return self._wrap("wald", mean, scale, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def weibull(self, a, size=None, chunks="auto", **kwargs):
        return self._wrap("weibull", a, size=size, chunks=chunks, **kwargs)

    @derived_from(np.random.RandomState, skipblocks=1)
    def zipf(self, a, size=None, chunks="auto", **kwargs):
        return self._wrap("zipf", a, size=size, chunks=chunks, **kwargs)


def _choice(state_data, a, size, replace, p):
    state = np.random.RandomState(state_data)
    return state.choice(a, size=size, replace=replace, p=p)


def _apply_random(RandomState, funcname, state_data, size, args, kwargs):
    """Apply RandomState method with seed"""
    if RandomState is None:
        RandomState = np.random.RandomState
    state = RandomState(state_data)
    func = getattr(state, funcname)
    return func(*args, size=size, **kwargs)


_state = RandomState()


seed = _state.seed


beta = _state.beta
binomial = _state.binomial
chisquare = _state.chisquare
if hasattr(_state, "choice"):
    choice = _state.choice
exponential = _state.exponential
f = _state.f
gamma = _state.gamma
geometric = _state.geometric
gumbel = _state.gumbel
hypergeometric = _state.hypergeometric
laplace = _state.laplace
logistic = _state.logistic
lognormal = _state.lognormal
logseries = _state.logseries
multinomial = _state.multinomial
negative_binomial = _state.negative_binomial
noncentral_chisquare = _state.noncentral_chisquare
noncentral_f = _state.noncentral_f
normal = _state.normal
pareto = _state.pareto
permutation = _state.permutation
poisson = _state.poisson
power = _state.power
rayleigh = _state.rayleigh
random_sample = _state.random_sample
random = random_sample
randint = _state.randint
random_integers = _state.random_integers
triangular = _state.triangular
uniform = _state.uniform
vonmises = _state.vonmises
wald = _state.wald
weibull = _state.weibull
zipf = _state.zipf

"""
Standard distributions
"""

standard_cauchy = _state.standard_cauchy
standard_exponential = _state.standard_exponential
standard_gamma = _state.standard_gamma
standard_normal = _state.standard_normal
standard_t = _state.standard_t
